Under-reporting of infectious gastrointestinal illness in British Columbia, Canada: who is counted in provincial communicable disease statistics?
Bibliographic record
Abstract
Under-reporting of infectious gastrointestinal illness (IGI) in British Columbia, Canada was calculated using simulation modelling, accounting for the uncertainty and variability of input parameters. Factors affecting under-reporting were assessed during a cross-sectional randomized telephone survey. For every case of IGI reported to the province, a mean of 347 community cases occurred (5th and 95th percentile estimates ranged from 181 to 611 community cases, respectively). Vomiting [odds ratio (OR) 2.15, 95% confidence interval (CI) 1.03-4.49] and antibiotic use in the previous 28 days (OR 3.59, 95% CI 1.17-10.97) significantly predicted health-care visits in a logistic regression model. In bivariate analyses, physicians were significantly less likely to request stool samples from patients with vomiting (RR 0.09, 95% CI 0.01-0.65) and patients of North American as opposed to non-North American cultural groups (RR 0.38, 95% CI 0.15-0.96). Physicians were more likely to request stool samples from older patients (P=0.003), patients with fewer household members (P=0.002) and those who reported anti-diarrhoeal use following illness (RR 3.33, 95% CI 1.32-8.45). People with symptoms of vomiting were under-represented in provincial communicable disease statistics. Differential degrees of under-reporting must be understood before biased surveillance data can be adjusted.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.031 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.012 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".